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Zhu, Siya; Arróyave, Raymundo (2026) Ground-state structure search of defective high-entropy alloys using machine-learning potentials and Monte Carlo sampling. Computational Materials Science, 270. doi:10.1016/j.commatsci.2026.114752

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Reference TypeJournal (article/letter/editorial)
TitleGround-state structure search of defective high-entropy alloys using machine-learning potentials and Monte Carlo sampling
JournalComputational Materials Science
AuthorsZhu, SiyaAuthor
Arróyave, RaymundoAuthor
Year2026 (June)Volume270
PublisherElsevier BV
DOIdoi:10.1016/j.commatsci.2026.114752Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID19933364Long-form Identifiermindat:1:5:19933364:8
GUID0
Full ReferenceZhu, Siya; Arróyave, Raymundo (2026) Ground-state structure search of defective high-entropy alloys using machine-learning potentials and Monte Carlo sampling. Computational Materials Science, 270. doi:10.1016/j.commatsci.2026.114752
Plain TextZhu, Siya; Arróyave, Raymundo (2026) Ground-state structure search of defective high-entropy alloys using machine-learning potentials and Monte Carlo sampling. Computational Materials Science, 270. doi:10.1016/j.commatsci.2026.114752
In(2026) Computational Materials Science Vol. 270. Elsevier BV

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